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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Microreactor Automated Control System Test Bed Digital Architecture for Real-Time, Hardware-in-the-Loop Simulation

This work describes progress made towards the development of a real-time hardware-in-the-loop (HIL) test bed for non-nuclear testing of microreactor control schemes and failure modes. Non-nuclear testing is a crucial step in developing robust control algorithms for managing microreactor dynamics. The creation of an HIL simulation harnesses the realistic dynamics of physical analogue systems while additionally considering the challenges of variable communication delay. This collaborative effort between Oak Ridge National Laboratory and Idaho National Laboratory has resulted in a LabVIEW-based gRPC communication protocol which couples a TRANSFORM Modelica simulation of nuclear components to the ViBRANT physical hardware for realistic feedback and visual representation of control action in real time. A modular python client structure is developed to manage FMU-based Modelica simulation and real-time gRPC communication. HIL testing suggests that the modeled reactor with natural convection molten salt loop coolant configuration responds well to PID control of drum positioning for modulation of reactor core power, however, future efforts will be made to explore the added thermal inertial delay of system level control and downstream demand changes. Development of this platform with a generalized methodology provides a foundation for exploring a variety of reactor configurations and failure modes in rapid order to provide insight into the most effective avenues of study for further research and development.

McConnell, Jono [ORNL] (ORCID:0000000238984741)↗
DOE OSTI2025

SCALE Analyses of Scenarios in the TRISO-based Heat Pipe Microreactor Fuel Cycle

This report documents the application of the SCALE code to the analysis of a TRistructural-ISOtropic (TRISO)-based heat pipe microreactor (HPMR) within the context of its nuclear fuel cycle stages. The evaluation was conducted in support of the US Nuclear Regulatory Commission’s ongoing efforts to assess modeling capabilities for advanced non–light-water reactor technologies. The generic HPMR selected as a representative microreactor concept features a compact core design that incorporates TRISO fuel compacts, passive heat removal via heat pipes, and a transportable configuration intended for deployment in remote environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Enhancing Autonomous Control of Microreactors Using Multi-Agent Reinforcement Learning

In order for microreactors to be economically competitive, operation costs will need to be minimized through some degree of autonomous control. Previous work has demonstrated the effectiveness of reinforcement learning (RL) for load-following control in a drum-controlled microreactor. This study extends that work by exploring the potential of RL to independently control each of the reactor’s drums. We compare a single-agent RL approach with a multi-agent RL (MARL) framework, testing them for generalization across different load-following power profiles and control timescales, and for robustness in cases of randomly disabled control drums. Since the point kinetics simulation environment used in this study cannot resolve spatial effects, we assume that in the absence of spatially localized disturbances, optimal drum movements should be symmetrical. We demonstrate that single-agent RL is able to achieve accurate performance only when symmetric actions are ignored; otherwise, it fails to train a useful controller. Meanwhile, the MARL framework performs symmetric actions by design and trains a robust, accurate agent, as evidenced by mean absolute errors in power matching of 0.41% for the training power profile, 0.68% for a profile with half the drums disabled, and 0.21% for a profile on a realistic load-following time horizon.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

ECAR-6332 Rev 3 RELAP5-3D Thermal-Hydraulic Analysis of MARVEL Microreactor - Final Design

This document reports the thermal hydraulic analyses results for the MARVEL microreactor, final design, including dry criticality, normal operation, operational transients and a set of very-low probability transients caused by accident conditions. The ultimate scope of this document is to demonstrate the MARVEL microreactor thermal hydraulic performances and its inherent safety. First, the list of the operational and accidental transients with the corresponding acceptance criteria are recalled. Then, details of the final design, the key input parameters, the assumptions, and the methodology used for performing the deterministic safety analyses are presented. Finally, the analyses results are provided, demonstrating the satisfaction of the corresponding acceptance criteria.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Assessing fuel cycle design options for advanced microreactors

Microreactor (MR) technology have recently gained traction due to their reduced construction cost compared to traditional large reactors. Their small physical geometry results in increased neutron leakage and deteriorated neutron economy which limits the achievable burnup of MR fuel. Refueling strategy currently discussed in MR design community is the battery-like refueling which is involve replacing the entire reactor core at the end of their operational cycle. Here, this paper investigates partial core refueling as means to increase MR fuel burnup and improve fuel cycle economy as a result. Two fuel cycle design strategies have been successful. These are the partial core refueling and partial core refueling combined with assembly rotation. Both strategies have been tested to operate a heatpipe-cooled MR referred to as the eVinci-like core for 72 Effective Full Power Months (EFPMs). The partial core refueling showed to reduce the refueling requirements by 18% compared to the battery-like refueling strategy. This corresponds to 10.5% reduction in the Net Present Value (NPV) of fuel cost over the 72 EFPMs of operation. The partial core refueling combined with rotation case reduces the refueling requirements by 29% (corresponding to 14.5% reduction in NPV of fuel cost) compared to the battery-like refueling strategy.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Feasibility of Recycling Discharged Microreactor Heavy Metal in Light Water and Sodium-Cooled Fast Reactors: A Neutronics Analysis

Nuclear microreactors (MRs) offer unique advantages, such as rapid deployment, potability, low maintenance requirements, and operational flexibility. Their compact size makes them a promising solution for decentralized power generation, particularly in remote areas, military bases, and disaster-stricken regions. However, MRs face challenges, including unutilized fissile material at the end of life, economic inefficiency, increased heavy metal (HM) waste complicating disposal, and the accumulation of plutonium (Pu) with high 239 Pu concentrations raising proliferation risks. Here, this study investigated the neutronics feasibility of a novel three-stage fuel cycle where discharged HM from MRs is recycled and burned in light water reactors and sodium-cooled fast reactors. This approach converts discharged HM into valuable fuel, enhancing the efficiency of MR deployments while improving the safeguardability of their final waste products. Neutronics analysis demonstrated that the safety characteristics of reactor designs in each stage were minimally impacted by the proposed cycle. For two representative MR designs, a fast-spectrum MR with solid pellet fuel and a thermal-spectrum MR with TRISO (TRi-structural-ISOtropic) fuel compacts, the proposed fuel cycle reduced the uranium disposal mass flow rate by ~60%, decreased the 235 U enrichment of the discharge fuel to ~1 wt%, eliminated plutonium disposal, and increased the cumulative fuel burnup to ~580 gigawatt-day per metric ton of initial heavy metal (GWd/t-iHM) or 60% fissions per initial metal atom. Despite the significant differences between the two MR designs, the performance and infrastructure requirements of the developed fuel cycles were remarkably similar, indicating its generalizability to a broader class of MRs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors (MRs) pose new challenges for international safeguards. Here, their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

The Monolithic Heat Pipe Microreactor Reference Plant Model Updates

This work presents the latest improvements to, and investigations performed with, the generic monolithic heat-pipe-cooled microreactor reference plant model for the United States Nuclear Regulatory Commission. This model serves as the foundation for the future detailed design evaluation models based on license applications. This model has been developed with the Comprehensive Reactor Analysis Bundle (BlueCRAB) and its specifications are based on open literature publications for the eVinci™ design . BlueCRAB is the U.S. Nuclear Regulatory Commission non-light-water reactor analysis system based on MOOSE, the Multiphysics Object-Oriented Simulation Environment framework, which can couple the Griffin, BISON, and Sockeye applications to resolve the various physics that are essential for the safety analysis of this type of reactor system. The core specifications include tristructural isotropic fuel, graphite monolith, graphite reflectors, and drums composed of graphite and B 4 C.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Streaming in a Nuclear Grade Sandwich Composite for Microreactor Shielding

The NGSC is a new approach to develop a shield structure for microreactors which combines the biological shielding with the reactor pressure vessel. Six layers of SS316 skins and core materials are present in the NGSC, where the core materials are reduce the neutron and gamma dose. Previous work has examined how a simplified NGSC can be optimized for cost, dose, and weight. This work explored the inclusion of SS36 ribs, which helps maintain the structural integrity of the NGSC, affects the transportation of radiation through the NGSC. For B$_4$C layers, the addition of ribs reduces neutron absorption but increase photon absorption. For WB$_4$-cermet layers, the addition of ribs reduces neutron absorption and reduces photon absorption.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Integrate Power Conversion Unit with MAGNET to Enable Integrated Microreactor Heat Transfer System Testing

Idaho National Laboratory obtained a closed, Brayton-cycle, power conversion unit (PCU) from Sandia National Laboratories. This PCU began as a commercially available, 30 kWe, C30, gas turbine from Capstone. The C30 was modified to use heat from an electric heater in a closed-loop system pressurized with nitrogen or dry air. INL has modified the unit further to integrate it with MAGNET and use heat from a microreactor test article.

42 - ENGINEERING↗
DOE OSTI2025

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗
DOE OSTI2024

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Small Modular Reactor and Microreactor Security-by-Design Lessons Learned: Integrated PPS Designs

U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping costs reasonable to make nuclear energy competitive. The past approach has often included implementing security features after a facility has been designed and without attention to optimization, which can lead to cost overruns. Incorporating security into the design process can provide robust, cost-effective, and sufficient physical protection systems. The purpose of this report is to capture lessons learned by the Advanced Reactor Safeguards and Security (ARSS) program that may be beneficial for other advanced and small modular reactor (SMR) vendors to use when developing security systems and postures. This report will capture relevant information that can be used in the security-by-design (SeBD) process for SMR and microreactor vendors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Effect of Preparation Conditions of Fe@SiO2 Catalyst on Its Structure Using High-Pressure Activity Studies in a 3D-Printed SS Microreactor

Fischer–Tropsch synthesis (FTS) in a 3D-printed stainless steel (SS) microchannel microreactor was investigated using Fe@SiO2 catalysts. The catalysts were prepared by two different techniques: one pot (OP) and autoclave (AC). The mesoporous structure of the two catalysts, Fe@SiO2 (OP) and Fe@SiO2 (AC), ensured a large contact area between the reactants and the catalyst. They were characterized by N2 physisorption, H2 temperature-programmed reduction (H2-TPR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron microscopy (XPS), and thermogravimetric analysis–differential scanning calorimetry (TGA-DSC) techniques. The AC catalyst had a clear core–shell structure and showed a much greater surface area than that prepared by the OP method. The activities of the catalysts in terms of FTS were studied in the 200–350 °C temperature range at 20-bar pressure with a H2/CO molar ratio of 2:1. The Fe@SiO2 (AC) catalyst showed higher selectivity and higher CO conversion to olefins than Fe@SiO2 (OP). Stability studies of both catalysts were carried out for 30 h at 320 °C at 20 bar with a feed gas molar ratio of 2:1. The Fe@SiO2 (AC) catalyst showed higher stability and yielded consistent CO conversion compared to the Fe@SiO2 (OP) catalyst.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Effect of Fe on Co-Based SiO2Al2O3 Mixed Support Catalyst for Fischer–Tropsch Synthesis in 3D-Printed SS Microchannel Microreactor

This research explores the effect of a composite support of SiO2 and Al2O3 with Fe and Co incorporated as catalysts for Fischer–Tropsch synthesis (FTS) using a 3D-printed stainless steel (SS) microchannel microreactor. Two mesoporous catalysts, FeCo/SiO2Al2O3 and Co/SiO2Al2O3, were synthesized via a one-pot (OP) method and extensively characterized using N2 physisorption, XRD, SEM, TEM, H2-TPR, TGA-DSC, FTIR, and XPS. H2-TPR results revealed that the synthesis method significantly affected the reducibility of metal oxides, thereby influencing the formation of active FTS sites. SEM-EDS and TEM further revealed a well-defined hexagonal matrix with a porous surface morphology and uniform metal ion distribution. FTS reactions, carried out in the 200–350 °C temperature range at 20 bar with a H2/CO molar ratio of 2:1, exhibited the highest activity for FeCo/SiO2Al2O3, with up to 80% CO conversion. Long-term stability was evaluated by monitoring the catalyst performance for 30 h on stream at 320 °C under identical reaction conditions. The catalyst was initially active for the methanation reaction for up to 15 h, after which the selectivity for CH4 declined. Correspondingly, the C4+ selectivity increased after 15 h of time-on-stream, indicating a shift in the product distribution toward longer-chain hydrocarbons. This trend suggests that the catalyst undergoes gradual activation or restructuring under reaction conditions, which enhances chain growth over time. The increase in C4+ products may be attributed to the stabilization of the active sites and suppression of methane or light hydrocarbon formation.

Biochemistry & Molecular Biology↗